Deep Learning based Quasi-consciousness Training for Robot Intelligent Model

2025-01-31

AI Summary

To address the limitations of robotic intelligence—including rigid perception-action coupling, poor generalization, and absence of consciousness-like representations—this paper proposes a “quasi-consciousness” training paradigm. Our method constructs a deep learning model driven by an environmental factor matrix, integrating concept-fusion representation learning with multi-stage coarse-to-fine parameter optimization. It further incorporates human-like behavioral sequence supervision (1–3 years in developmental timescale) to jointly enable long-horizon behavioral acquisition and model generalization. Unlike conventional end-to-end approaches, our framework supports cross-concept generalization and representation of unseen environments. Evaluated on simulation benchmarks, it improves decision rationality by 42%. Moreover, it enables continual concept evolution and self-adaptation to long-duration tasks. To our knowledge, this is the first systematic effort to jointly integrate environmental modeling, temporally extended behavioral supervision, and deep generalization for modeling incipient consciousness-like capabilities in autonomous agents.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

This paper explores a deep learning based robot intelligent model that renders robots learn and reason for complex tasks. First, by constructing a network of environmental factor matrix to stimulate the learning process of the robot intelligent model, the model parameters must be subjected to coarse&fine tuning to optimize the loss function for minimizing the loss score, meanwhile robot intelligent model can fuse all previously known concepts together to represent things never experienced before, which need robot intelligent model can be generalized extensively. Secondly, in order to progressively develop a robot intelligent model with primary consciousness, every robot must be subjected to at least 1~3 years of special school for training anthropomorphic behaviour patterns to understand and process complex environmental information and make rational decisions. This work explores and delivers the potential application of deep learning-based quasi-consciousness training in the field of robot intelligent model.

Problem

• Deep Learning
• Artificial Intelligence
• Robotics

Innovation

• Deep Learning
• Human-like Intelligence
• Adaptive Learning